Are Final Comments in Web Survey Panels Associated with Next-Wave Attrition?
Bibliographic record
Abstract
Near the end of a web survey respondents are often asked whether they have further comments. Such final comments are usually ignored, in part because open-ended questions are challenging to analyse. We explored whether final comments are associated with next-wave attrition in survey panels. We categorized a random sample of final comments in the Longitudinal Studies for the Social Sciences (LISS) panel and Dutch Immigrant panel into one of eight categories (neutral, positive, six subcategories of negative) and regressed the indicator of next-wave attrition on comment length, comment category and socio-demographic variables. In the Immigrant panel we found shorter final comments (55 words) with decreased next-wave attrition relative to making no comment. Comments about unclear survey questions quadruple the odds of attrition and “other” (uncategorized) negative comments almost double the odds of attrition. In the LISS panel, making a comment (vs. not) and comment length are not associated with attrition. However, when specifying individual comment categories, neutral comments are associated with half the odds of attrition relative to not making a comment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.082 | 0.354 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".